Machine learning prediction of obesity-associated gut microbiota: identifying as a potential therapeutic target.

Journal: Frontiers in microbiology
Published Date: Feb 5, 2025

Abstract

BACKGROUND: The rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies.

Authors

  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Yuxuan Jiang
    Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, China.
  • Xinran Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Shenglan Wang
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Changle Zhao
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Ximiao Yang
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Baocheng Chang
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Juhong Yang
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Jianjun Qiao
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals